init ml

ml/tensorflow/test.py

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+import sys
+import tensorflow.examples.tutorials.mnist.input_data as input_data
+import tensorflow as tf
+
+def print_tensor(te):
+    init_op = tf.global_variables_initializer()
+    #run the graph
+    with tf.Session() as sess:
+        sess.run(init_op) #execute init_op
+        print (sess.run(te))
+
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+x = tf.placeholder("float", [None, 784])
+
+W = tf.Variable(tf.zeros([784,10]))
+b = tf.Variable(tf.zeros([10]))
+
+print_tensor(b)
+
+t = tf.matmul(x, W)
+y = tf.nn.softmax(t + b)
+
+y_ = tf.placeholder("float", [None,10])
+
+cross_entropy = -tf.reduce_sum(y_*tf.log(y))
+
+train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+
+
+#init = tf.initialize_all_variables()
+init = tf.global_variables_initializer()
+sess = tf.Session()
+sess.run(init)
+
+for i in range(1000):
+    batch_xs, batch_ys = mnist.train.next_batch(100)
+    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
+
+correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
+
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+
+print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
+

ml/vw/README.md

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+# Demo
+
+## Introduction
+
+https://github.com/JohnLangford/vowpal_wabbit/wiki/Tutorial
+
+## Examples
+
+```
+vw house_dataset -l 10 -c --passes 25 --holdout_off
+vw house_dataset -l 10 -c --passes 25 --holdout_off -f house.model
+
+vw house_dataset -p /dev/stdout --quiet
+
+vw -i house.model -t house_dataset -p /dev/stdout --quiet
+vw house_dataset --audit --quiet
+
+
+```

ml/vw/house_dataset

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+0 | price:.23 sqft:.25 age:.05 2006
+1 2 'second_house | price:.18 sqft:.15 age:.35 1976
+0 1 0.5 'third_house | price:.53 sqft:.32 age:.87 1924

ml/vw/overfitrun.sh

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+vw house_dataset -l 10 -c --passes 25 --holdout_off

ml/vw/run.sh

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+vw house_dataset